231 COVID-19 Lockdown Policies Across 20 Countries Modulate Sleep and Resting Heart Rate Measures

• Published in: Sleep (New York, N.Y.) Vol. 44; no. Supplement_2; p. A92
• Main Authors: Ong, Ju Lynn, Lau, Teyang, Karsikas, Mari, Kinnunen, Hannu, Chee, Michael
• Format: Journal Article
• Language: English
• Published: Westchester Oxford University Press 03.05.2021
• Subjects: A. Basic and Translational Sleep and Circadian Science; Coronaviruses; COVID-19; Heart rate; Shelter in place; Sleep
• ISSN: 0161-8105; 1550-9109
• DOI: 10.1093/sleep/zsab072.230

Abstract Introduction Lockdowns imposed to stem the spread of COVID-19 have disrupted the lifestyles of many worldwide, but studies to date are mostly confined to observations within a limited number of countries, based on subjective reports and survey from a narrow time window. In the present study, we investigate associations between the severity of lockdown policies and objective sleep and resting-heart rate measures. Methods Data from 113,000 users of a consumer sleep tracker across 20 countries were gathered between Jan–Jul 2020 and compared with an equivalent period in 2019 as a control for naturally occurring seasonal fluctuations. Lockdown stringency was derived using scores from the Oxford Government Response Tracker. Multilevel growth curve models were used to quantify the effect of lockdown stringency on changes to sleep patterns (midsleep time and midsleep variability) and resting heart rate changes, and to predict changes in resting heart rate from changes to sleep patterns. Results Lockdown severity modulated the size of shifts in sleep midpoint and regularity during this period. Midsleep times were delayed in all countries during strict lockdowns, particularly on weekdays, while midsleep variability reduced. The largest shifts in midsleep time (+0.09 to +0.58 hours), midsleep variability (–0.12 to –0.26 hours) and resting heart rate (–0.35 to –2.08 bpm) occurred in April and May - when most countries imposed their strictest lockdown measures. In addition, multilevel modelling revealed that for each unit increase in stringency index, midsleep time was delayed by 0.96 min, midsleep variability decreased by 0.46 min and resting heart rate decreased by 0.06 bpm. Finally, in models predicting changes in resting heart rate from changes to sleep patterns, midsleep variability was shown to be the strongest predictor of resting heart rate, wherein an hour increase in the standard deviation of midsleep variability predicted a 5.12 increase in bpm, while an hour increase in midsleep time only predicted a 1.25 decrease in bpm. Conclusion Our findings demonstrate the utility of large-scale data from consumer wearables in providing population-level insights into how lockdown severity directly impacts sleep health during this pandemic period. Support (if any) Work conducted at NUS is supported by a grant awarded to Michael Chee (NMRC/STAR19may-0001).